Table 4

Final scale – factor loadings, reliability, and variance explained (N = 200)

FactorsItemFactor loadingsCronbach's αVariance explained, %
Factor 1: Data protection distrustI am concerned that my personal and academic data could be shared with third parties without my permission. (V1)0.8270.81224.325
I am afraid that stored and processed data might be changed or manipulated without authorization. (V2)0.811
I am concerned that my stored information could be used for purposes other than those originally intended without my knowledge or consent. (V4)0.876
Factor 2: Functional distrustI doubt that the suggestions or recommendations it provides effectively support my learning needs. (V5)0.7960.70621.475
I am concerned that the technology may not adequately address specific learning goals or objectives. (V6)0.801  
I am sceptical about the tool's ability to deliver meaningful educational outcomes for me. (V8)0.787  
Factor 3: Distrust of AI replacing humansA trainer with extensive experience can teach more effectively than an AI. (V10)0.8550.75222.685
Experienced trainers have a deeper understanding of the entrepreneurial process than AI can achieve. (V11)0.787  
A human trainer can better understand my emotional needs and adapt their teaching accordingly, which AI tools cannot do. (V13)0.781  
Total variance explained (%)  68.485

Note(s): KMO = 0.709

Extraction method: Principal Component Analysis

Rotation method: Varimax with Kaiser normalization

Source(s): Authors’ own work

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